This article was independently developed by The Economy editorial team and draws on original analysis published by East Asia Forum. The content has been substantially rewritten, expanded, and reframed for broader context and relevance. All views expressed are solely those of the author and do not represent the official position of East Asia Forum or its contributors.

This article is based on ideas originally published by VoxEU – Centre for Economic Policy Research (CEPR) and has been independently rewritten and extended by The Economy editorial team. While inspired by the original analysis, the content presented here reflects a broader interpretation and additional commentary. The views expressed do not necessarily represent those of VoxEU or CEPR.

I have spent years in AI and data science, believing that structured models and quantitative analysis were the future. That perspective changed the moment I became a target of an orchestrated misinformation campaign—one that wasn’t random but designed to destroy my credibility, my institution’s reputation, and my work.

GIAI's primary research objective with the coming cycle's of MSc AI/Data Science is to build a graph-based Shapley Value for HR contribution analysis.

Many amateur data scientists have little respect to math/stat behind all computational modelsMath/stat contains the modelers' logic and intuition to real world data

Top brains in AI/Data Science are driven to challenging jobs like modelingSeldom a 2nd-tier company, with countless malpractices, can meet the expectations

People following AI hype are mostly completely misinformedAI/Data Science is still limited to statistical methodsHype can only attract ignorance As a professor of AI/Data Science, I from time to time receive emails from a bunch of hyped followers claiming what they call 'recent AI' can solve things that I have been pessimistic. They usually think 'recent AI' is close to 'Artificial General Intelligence', which means the program learns by itself and it is beyond human intelligence level.

Math in AI/Data Science is not really math, but a shortened version of English paragraph.

Korean GDP growth was 6.4%/y for 50 years until 2022, but down to 2.1%/y in 2020s.Due to low birthrate down to 0.7, population is expected to 1/2 in 30 years.Policy fails due to nationwide preference to leftwing agenda.

Transition from column to matrix, matrix to tensor as a baseline of data feeding changed the scope of data science,

When an expectation for future is shared, market reflects it immediatelyUS Fed hints to lower interest rates in March, which is already reflected in prices

Web novel to Webtoon conversion is not only based on 'profitability'If the novel author is endowed with money or bargaining power, 'Webtoonization' may be nothing more than a marketting tool for the web novel.

Not the quality of teaching, but the way it operatesEasier admission and graduation bar applied to online degrees

Asian companies convert degrees into years of work experienceWithout adding extra values to AI degree, it doesn't help much in salary

The relationship between a commercial district and the concentration of consumers in a specific generation mostly is not by causal effectSimultaneity oftern requires instrumental variables

One-variable analysis can lead to big errors, so you must always understand complex relationships between various variables. Data science is a model research project that finds complex relationships between various variables. Obsessing with one variable is a past way of thinking, and you need to improve your way of thinking in line with the era of big data. When providing data science speeches, when employees come in with wrong conclusions, or when I give external lectures, the point I always emphasize is not to do 'one-variable regression.'

With high variance, 0/1 hardly yields a decent model, let alone with new set of dataWhat is known as 'interpretable' AI is no more than basic statistics